10 DAX Calculations for your Tabular or Power Pivot Model (Part 2)

This is part two of my 10 DAX Calculations blog series. In this series I’ve blogged ten different DAX calculations you may find useful in your Power Pivot, Tabular or Power BI model. So if you missed part one, I would encourage you to check that out.

So here’s five more DAX calculations (in no particular order) that I hope you will find useful. Enjoy!

5. DAX Use an Inactive/Custom Relationships in a Calculation

In Tabular and Power Pivot models, you are limited to only one Active relationship between two tables. Often in a relational database you’ll have a role-playing table or a table that is related to another table with more than one foreign-key. A common example of a role-playing table is a Date dimension table since our fact tables often have multiple dates, which is what the following screenshot depicts. We can see that there are two relationships between the Sales and Date tables.

The solid line indicates that this is an Active relationship. The dotted line indicates that relationship is Inactive. In the Manage Relationships window, you can clearly see that one of the relationships is Inactive (highlighted in red). What this means for us is that during slicing, dicing and filtering the Active relationship is used.

The good news is that we can still build calculations that leverage Inactive or unspecified relationships in our model! To do this we can use the USERELATIONSHIP function. This function allows us to specify which columns to relate two tables together.

Here’s an example of a calculation for Total Quantity that uses the relationship highlighted in red in the above screenshot:

Total Quantity by Update Date:=CALCULATE([Total Quantity],USERELATIONSHIP(Sales[UpdateDate],'Date'[Datekey]))

So when we slice the Total Quantity by Update Date calculation by the Date table, the relationship used is the not the default Active relationship as is the case with the Total Quantity measure. We can clearly see this illustrated below in the pivot table:

4. DAX Semi-Additives Measures Opening Period and Closing Period Calculations

Sometimes we have a requirement to create a semi-additive type calculation. If you’re familiar with creating SSAS multidimensional cubes, you’re probably familiar with the concept of semi-additive measure. A semi-additive measure is a measure that is not fully aggregate-able meaning that to find the correct measure amount we cannot aggregate the measure across all time. Consider a measure for account balances or product inventory. If we wish to calculate the current product inventory for the year we must return the inventory on the last day of the year instead of summing the account balances for each day in the year.

There are a couple useful DAX functions we can use to find the opening period balance/inventory or the closing period balance/inventory: FIRSTNONBLANK and LASTNONBLANK.

Here is an example of an opening period type calculation. This calculation will return the first non-blank value in a given date range. Ideally, this will be the product inventory on the first day of the month or year.

3. DAX Previous Year, Previous Month, Previous Quarter Calculations

In part 1 we looked briefly how to calculate the previous MTD calculation, but these functions can also just be used to calculate the previous period metrics and compare the previous period to the current period.

Here’s a couple of examples of using PREVIOUSMONTH and PREVIOUSYEAR to calculate the Starting Inventory for the previous periods:

In the query results we can now see that we’re able to calculate the previous month and previous year starting inventory amounts. Excellent!

2. DAX First Day of Year, Quarter or Month Inventory/Balance Calculations

The previous examples in this post have all been calculations relative to the level of the Calendar Hierarchy being browsed, but sometimes we just need to calculate the measure on the first day of the year, quarter or month no matter what level we’re browsing. For those situations, we can use the following functions:

And now we have the Inventory Amounts for the first day of the year or quarter:

1. DAX Rolling Sum and Average Calculations

This type of calculation is easier to set in a multidimensional cube in my opinion but with that said I am more of a cube guy. To get this working, I first created a calculation that sums the total Sales Amount for each day in the past 90 days.

Rolling 90 Day Total Sales Amount:
= CALCULATE([Total Sales Amount],
DATESBETWEEN('Date'[Datekey],
FIRSTDATE( /* FIRSTDATE gets the first date in this date range which will be 90 days ago */
DATEADD( /* DATEADD lets me navigate back in time */
LASTDATE('Date'[Datekey]) /* This returns the last day no matter if I'm at the day, month or year level */
,-89,Day
)
),
LASTDATE('Date'[Datekey]) /* Use the max date for the end date */
)
)

Review the comments in the code above to get a better idea of what all the functions are being used for.

Then I created a measure that counts the days that exist within the past 90 days. If the day I’m counting is the first day in my calendar that I have data, I only want to count that one day. This calculation is very similar to the previous calculation except I’m using the COUNTX function to count the rows returned.

Now that I have a calculation to get the rolling total sales amount and count the days all I need to do is simply divide the two numbers:

Rolling 90 Day Average Sales Amount:
=DIVIDE([Rolling 90 Day Total Sales Amount],[DayCount])

And here’s the query results:

Notice that for January the Day Count measure shows only 31. This is because prior to January there’s not data so the Rolling Average is only considering days where data exists. Also notice that the numbers for March and Q1 match since both periods should have the same amount of sales and the same number of days.

Resources

Feedback?

Let me know your thoughts on these calculations! There’s 100 ways to skin a cat so if you have a different or better way to perform some of these calculations I’d love to see your solution! Thanks for reading! 🙂

I think in this particular instance its not necessary but I wrote this 4 months ago and can’t remember exactly what I was thinking at the time. I believe it might be an artifact left over from a different calculation I used.

Hi Alex, its hard to say without seeing your model and your data, but verify that for each Application Offer Sent Date in your Application table there is a corresponding Date in the date table. Another way to verify the relationship would be to temporarily make the inactive relationship Active just to validate your numbers. Hope this helps.

Thanks for the reply. After further investigations, I found that the time stamps in my Application table caused the issue. That is because they were unable to match the 12:00:00 AM time stamp in my Date table. As a work around, I have created additional columns that only include the date part of the Application Offer Sent Date, and have used that to set up the relationship.

So simple… yet overlooked. the USERELATIONSHIP function was a lifesaver.

I was recreating an entire table, summarizing it and then creating a Date relationship based on a second column. Now I have 2 relationships where one is used to slice the table, and the second one is used for a single set of calculated fields.